not-ner-v2_16batch

This model is a fine-tuned version of dccuchile/tulio-chilean-spanish-bert on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1124
• Accuracy: 0.9662
• Precision: 0.9671
• Recall: 0.9662
• F1: 0.9665

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.0906	0.0798	250	0.1124	0.9662	0.9671	0.9662	0.9665
0.1008	0.1596	500	0.1416	0.9654	0.9652	0.9654	0.9653
0.0918	0.2394	750	0.1361	0.9662	0.9657	0.9662	0.9659
0.1077	0.3192	1000	0.1353	0.9636	0.9626	0.9636	0.9627
0.0863	0.3990	1250	0.1457	0.9590	0.9624	0.9590	0.9601
0.1048	0.4788	1500	0.1340	0.9653	0.9645	0.9653	0.9648
0.1066	0.5586	1750	0.1490	0.9631	0.9621	0.9631	0.9624
0.107	0.6384	2000	0.1479	0.9638	0.9641	0.9638	0.9639
0.1274	0.7182	2250	0.1331	0.9636	0.9634	0.9636	0.9635
0.1381	0.7980	2500	0.1577	0.9626	0.9616	0.9626	0.9618
0.1111	0.8778	2750	0.1353	0.9625	0.9637	0.9625	0.9630
0.132	0.9575	3000	0.1785	0.9592	0.9578	0.9592	0.9579
0.1464	1.0373	3250	0.1712	0.9610	0.9598	0.9610	0.9600
0.1279	1.1171	3500	0.1514	0.9630	0.9622	0.9630	0.9625
0.0947	1.1969	3750	0.1554	0.9625	0.9629	0.9625	0.9627
0.0958	1.2767	4000	0.1746	0.9617	0.9606	0.9617	0.9607
0.1212	1.3565	4250	0.1494	0.9628	0.9620	0.9628	0.9622
0.1097	1.4363	4500	0.1557	0.9635	0.9627	0.9635	0.9630
0.1288	1.5161	4750	0.1499	0.9642	0.9633	0.9642	0.9635
0.1043	1.5959	5000	0.1488	0.9614	0.9619	0.9614	0.9617
0.1016	1.6757	5250	0.1521	0.9616	0.9611	0.9616	0.9613
0.1073	1.7555	5500	0.1455	0.9618	0.9621	0.9618	0.9619
0.1264	1.8353	5750	0.2369	0.9143	0.9407	0.9143	0.9218
0.1211	1.9151	6000	0.1524	0.9613	0.9606	0.9613	0.9609
0.1071	1.9949	6250	0.1327	0.9574	0.9565	0.9574	0.9548
0.0915	2.0747	6500	0.1325	0.9640	0.9638	0.9640	0.9639
0.084	2.1545	6750	0.1321	0.9647	0.9646	0.9647	0.9646
0.0922	2.2343	7000	0.1486	0.9644	0.9635	0.9644	0.9637
0.0877	2.3141	7250	0.1281	0.9643	0.9648	0.9643	0.9645
0.0828	2.3939	7500	0.1425	0.9575	0.9591	0.9575	0.9582
0.0859	2.4737	7750	0.1283	0.9670	0.9665	0.9670	0.9667
0.0866	2.5535	8000	0.1189	0.9677	0.9671	0.9677	0.9673
0.0681	2.6333	8250	0.1195	0.9689	0.9682	0.9689	0.9683
0.0462	2.7131	8500	0.1408	0.9678	0.9670	0.9678	0.9670
0.0672	2.7929	8750	0.1233	0.9690	0.9684	0.9690	0.9686
0.0578	2.8726	9000	0.1232	0.9694	0.9689	0.9694	0.9691
0.0591	2.9524	9250	0.1219	0.9694	0.9689	0.9694	0.9690

Framework versions

• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1